Tag Archives: Data paucity

An IT industry analyst article published by SearchITOperations.

Securing IoT data should become a priority as more companies manipulate the volumes produced by these devices. Seemingly innocuous information could allow privacy invasions.

Mike Matchett

The data privacy and access discussion gets all the more complicated in the age of IoT.

Some organizations might soon suffer from data paucity — getting locked, outbid or otherwise shut out of critical new data sources that could help optimize future business. While I believe that every data-driven organization should start planning today to avoid ending up data poor, this concern is just one of many potential data-related problems arising in our new big data, streaming, internet of things (IoT) world. In fact, issues with getting the right data will become so critical that I predict a new strategic data enablement discipline will emerge to not just manage and protect valuable data, but to ensure access to all the necessary — and valid — data the corporation might need to remain competitive.

In addition to avoiding debilitating data paucity, data enablement will mean IT will also need to consider how to manage and address key issues in internet of things data security, privacy and veracity. Deep discussions about the proper use of data in this era of analytics are filling books, and much remains undetermined. But IT needs to prepare for whatever data policies emerge in the next few years.

Piracy or privacy?

Many folks explore data privacy in depth, and I certainly don’t have immediate advice on how to best balance the personal, organizational or social benefits of data sharing, or where to draw a hard line on public versus private data. But if we look at privacy from the perspective of most organizations, the first requirements are to meet data security demands, specifically the regulatory and compliance laws defining the control of personal data. These would include medical history, salary and other HR data. Many commercial organizations, however, reserve the right to access, manage, use and share anything that winds up in their systems unless specifically protected — including any data stored or created by or about their employees.

If you are in the shipping business, using GPS and other sensor data from packages and trucks seems like fair game. After all, truck drivers know their employers are monitoring their progress and driving habits. But what happens when organizations track our interactions with IoT devices? Privacy concerns arise, and the threat of an internet of things security breach looms.

Many people are working hard to make GPS work within buildings, ostensibly as a public service, using Wi-Fi equipment and other devices to help triangulate the position of handheld devices and thus locate people in real time, all the time, on detailed blueprints.

In a shopping mall, this tracking detail would enable directed advertising and timely deals related to the store a shopper enters. Such data in a business setting could tell your employer who is next to whom and for how long, what you are looking at online, what calls you receive and so on. Should our casual friendships — not to mention casual flirting — bathroom breaks and vending machine selections be monitored this way? Yet the business can make the case that it should be able to analyze those associations in the event of a security breach — or adjust health plan rates if you have that candy bar. And once that data exists, it can be leaked or stolen…(read the complete as-published article there)

An IT industry analyst article published by SearchITOperations.

Big data is out there, waiting to make you rich — or help your organization succeed anyway. But there are still more unknowns than knowns about the future of big data.

Mike Matchett

Big data is being created everywhere we look, and we are all thinking about how to take advantage of it. I certainly want to come up with some novel new big data application and become fabulously wealthy just for the idea. The thing is, most companies — perhaps all — can profit from big data today just by accelerating or refining some piece of their current business, supposing they can identify and corral the right information in the right time and place.

There is no need to find a new earth-shattering application to get started. I believe a significant big data payback is right in front of any marketing, sales, production or customer-engagement team. One simply needs to find a way to unlock the buried big data treasure. And, of course, that’s where big data concerns from practical to theoretical bubble to the surface.

A big sticking point has been finding the data science expertise, especially experts who could build optimized machine learning models tailored for your exact business needs. But we are seeing some interesting efforts recently to automate and, in some ways, commoditize big data handling and complicated machine learning. These big data automation technologies enable the regular Java Joe or Josie programmer to effectively drop big data analytics into existing, day-to-day operational-focused business applications.

Not only does this have the democratizing effect of unlocking big data value for non-data scientists, but it also highlights the trend toward a new application style. In the next three to five years, we will see most business applications that we’ve long categorized as transactional converge with what we’ve implemented separately as analytical applications. Put simply, with big data power, “business intelligence” is becoming fast enough and automated enough to deliver inside the operational business process in active business timeframes.

As these data processing worlds collide, they will reveal big data concerns for IT staff, and for those making decisions on IT infrastructure and data centers. Storage, databases and even networks will all need to adapt. Along with the rise of the internet of things (IoT), hybrid cloud architectures, persistent memory and containers, 2017 is going to be a pivotal year for challenging long-held assumptions and changing IT directions.
Out-of-reach data

While I will undoubtedly focus a lot of time and energy as an industry analyst on these fast-evolving topics in the near term, there is a longer-term big data concern: Some companies might not be able to take advantage of this democratization of data simply because they can’t get access to the data they need.

We need to think about how we can ensure [big data is] reliable, how we can maintain and ensure privacy — and regulatory compliance — how we can ensure we only implement ethical and moral big data algorithms and so on.

We’ve heard warnings about how hard it is to manage big data as important data. We need to think about how we can ensure it’s reliable, how we can maintain and ensure privacy — and regulatory compliance — how we can ensure we only implement ethical and moral big data algorithms and so on. But before all that, you first need access to the data — assuming it exists or can be created — that is valuable to your company. I call this the data paucity problem — there’s too little big data in use.

As an example, I don’t believe every IoT device manufacturer will end up getting unfettered access to the data streams generated by their own things, much less to the ecosystem of data surrounding their things in the field. I think it is inevitable that some will be getting locked out of their own data flowback…(read the complete as-published article there)

RT @TruthinIT: There's no cost of goods like a traditional NAS device where I've got disks I've got to pay for. And if I'm not using the data on those disks, I still got to pay for those disks. bit.ly/2BBX073@Nasuni@smworldbigdata

In 30 min I'm interviewing @Cohesity (and customer) on @TruthinIT about Mass Data Fragmentation. It's about having too many copies in about four or five different "dimensions", including cloud! Join us webcast (12.11.18) @ 1pmET (and there will be prizes) bit.ly/2PdqrQn